CyberFreezeDev
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Cameras, sensors and document pipelines linking shop floor, stores and accounts, on hardware inside the plant.
The losses are rarely dramatic. They are small, daily and unrecorded.
Here, automation means a camera that flags defects within seconds, a sensor that logs every stoppage and a pipeline that matches supplier invoices to deliveries.
On the shop floor, quality checks are done by eye at the end of the line. A defect that started at 10 a.m. is found at 4 p.m., after a whole shift of parts. Machine stoppages are written in a register, if at all, so nobody can say which machine loses the most hours or why. Supervisors send the day's production numbers as a WhatsApp message that someone types into a spreadsheet the next morning.
In the stores and accounts office the problem is paper. Each supplier delivery comes with a challan and an invoice that must be checked against the purchase order and the goods received note before it is paid. Done by hand, that three-way match is slow, so either suppliers are paid late or invoices are paid without being checked properly.
Manufacturing automation here does not mean robots. It means a camera that flags a defect within seconds, a sensor that records every stoppage, and a pipeline that reads supplier invoices and matches them to what actually arrived.
We pick the smallest device that does the job reliably. Everything runs on the plant network and keeps working if the internet drops.
| Problem | What we install | Runs on | What you get |
|---|---|---|---|
| Defects found late | Camera above the line with a trained vision model | NVIDIA Jetson or Google Coral | Defect flagged with a photo, line stopped or part diverted |
| Unrecorded stoppages | Current or vibration sensor on each machine | Raspberry Pi gateway | Run and idle time per machine, reason code entered by the operator |
| Counts typed from WhatsApp | Structured shift report on WhatsApp | Cloud or plant server | Daily production sheet built automatically |
| Temperature or humidity drift | Sensors in ovens, cold rooms or curing areas | Raspberry Pi gateway | Alert to the supervisor on WhatsApp when a limit is crossed |
| SOPs nobody can find | Assistant that answers from your manuals | Private server in the plant | Answers with the page it came from |
Vision models are trained on photos of your own good and defective parts. We measure accuracy on a held-back set before the camera is trusted on the line, and share the results with you.
The same factory loses as much time in the office as on the floor.
Supplier invoices are checked against the PO and goods received note by item, quantity and rate.
Supplier invoices are read by our OCR pipeline, then matched to the purchase order and the goods received note by item, quantity and rate. Differences are listed for the purchase team rather than paid.
Before dispatch, each invoice is checked for an IRN and an e-way bill where required.
Before dispatch, the system checks that each B2B invoice has an IRN where e-invoicing applies and that an e-way bill exists for consignments above the threshold. Invoices, expenses and GST summaries can be kept in Fint or posted to Tally.
Invoices from micro and small suppliers tracked against the MSMED Act payment deadline.
Invoices from registered micro and small suppliers are tracked against the payment deadline under the MSMED Act, so they are paid on time and the deduction is not lost.
Purchase requests and payment releases approved on email or WhatsApp, with an audit trail.
Purchase requests, rate changes and payment releases go through an approval flow on email or WhatsApp with a full audit trail, built through our n8n integrations work.
Plant networks drop, so inspection and sensor logic run on edge devices on the line, and only summaries go to the cloud.
Plant networks drop, and a quality camera that stops when the internet goes down is worse than none. So inspection and sensor logic run on edge devices on the line, and only summaries go to the cloud. Our lab works with NVIDIA Jetson, Raspberry Pi and Google Coral boards, IoT sensors and local language models, which is the work behind our computer vision and edge AI development services. Our lab notes on edge AI and IoT explain which decisions belong on the device.
For manuals, drawings and supplier contracts that should not leave the company, we set up a language model on a server inside the plant so engineers can ask questions without the files going to an outside AI service. See private AI deployment. Projects usually begin with a short visit to see the line, then one camera or one machine as a pilot.
It depends on the defect, the lighting and how many example photos exist. We collect images of your own parts, train the model, and test it on photos it has not seen before giving you a measured result. If the numbers are not good enough for the line, we say so before any hardware is installed for production.
No. Clip-on current sensors or vibration sensors work on most older machines. They tell us when a machine is running, idle or stopped, and the operator adds the reason for a stoppage on a tablet or by WhatsApp.
Yes. Inspection and sensor logging run on devices inside the plant and store data locally. Summaries sync to the cloud when the connection returns.
Yes. We build integrations with Tally, so matched supplier invoices and approved entries can be posted as import files instead of being typed again.
No. We work on cameras, sensors, edge computers and software. Robot arms and machine control stay with your automation vendor.
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